Machine learning–based prediction and quality control of meat and meat products: applications for intelligent quality management in the meat industry

The quality of meat and meat products critically affects consumer acceptance, market value, and food safety. Traditional quality evaluation methods rely on labor-intensive, destructive, and time-consuming analytical techniques, limiting their application in real-time industrial environments. Recent advances in machine learning (ML) have enabled the rapid, accurate, and nondestructive prediction of food quality attributes. This review explores the current applications of ML technologies for predicting and managing the quality of livestock-derived foods. Various data acquisition methods, including spectroscopy, imaging technologies, electronic sensors, and biochemical analyses, are reviewed as key data sources for ML-based prediction models. In addition, commonly used algorithms such as support vector machines, random forests, artificial neural networks, and deep learning architectures are reviewed in relation to their performance in predicting meat quality parameters, microbial contamination, shelf life, and authenticity. Combining ML models with sensor technologies and industrial processing systems has enabled the development of intelligent quality control frameworks for livestock products. Despite significant progress, challenges remain regarding data standardization, model interpretability, and large-scale industrial implementation. Future research should focus on the integration of multimodal data, digital twins, and advanced artificial intelligence technologies for next-generation smart food processing systems. ML-driven quality prediction systems are expected to become increasingly important in improving the efficiency, safety, and sustainability of the livestock food industry.